ASSIGNING AN INDEXING WEIGHT TO A SEARCH TERM

• US 20100153366A1
• Filed: 12/15/2008
• Published: 06/17/2010
• Est. Priority Date: 12/15/2008
• Status: Abandoned Application

• Current Assignee: Motorola Mobility, Inc. (Lenovo Group Ltd.)
• Original Assignee: Motorola, Inc. (Motorola Solutions, Inc.)
• Inventors: Liu, Chen
• Application Number:
  US12/334,842
• Publication Number:
  US 20100153366A1
• Time in Patent Office: Days
• US Class Current: 707/722
• CPC Class Codes: G06F 16/313 Selection or weighting of terms for indexing

Abstract

Disclosed is an indexing weight assigned to a potential search term in a document, the indexing weight is based on both textual and acoustic aspects of the term. In one embodiment, a traditional text-based weight is assigned to a potential search term. This weight can be TF-IDF (“term frequency-inverse document frequency”), TF-DV (“term frequency discrimination value”), or any other text-based weight. Then, a pronunciation prominence weight is calculated for the same term. The text-based weight and the pronunciation prominence weight are mathematically combined into the final indexing weight for that term. When a speech-based search string is entered, the combined indexing weight is used to determine the importance of each search term in each document. Several possibilities for calculating the pronunciation prominence are contemplated. In some embodiments, for pairs of terms in a document, an inter-term pronunciation distance is calculated based on inter-phoneme distances.

Claims

1. A method for assigning an indexing weight to a search term in a document, the document in a collection of documents, the method comprising:

calculating a text-based indexing weight for the search term in the document;

calculating a pronunciation prominence for the search term; and

assigning an indexing weight to the search term in the document, the indexing weight based, at least in part, on a mathematical combination of the calculated text-based indexing weight and the calculated pronunciation prominence.

2. The method of claim 1 wherein calculating a text-based indexing weight for the search term in the document comprises:

calculating a term frequency for the search term in the document;

calculating an inverse document frequency for the search term in the collection of documents; and

calculating the text-based indexing weight for the search term in the document by mathematically combining the calculated term frequency and the calculated inverse document frequency.

3. The method of claim 1 wherein calculating a text-based indexing weight for the search term in the document comprises:

calculating a term frequency for the search term in the document;

calculating a discrimination value for the search term in the collection of documents; and

calculating the text-based indexing weight for the search term in the document by mathematically combining the calculated term frequency and the calculated discrimination value.

4. The method of claim 1 wherein calculating a pronunciation prominence for the search term comprises:

translating terms in the documents in the collection of documents into phonetic pronunciations;

calculating inter-term pronunciation distances between pairs of the translated terms, the calculating based, at least in part, on inter-phoneme distances; and

calculating the search term pronunciation prominence, the calculating based, at least in part, on inter-term pronunciation distances.

5. The method of claim 4 further comprising:

calculating an inter-phoneme distance, the calculating based, at least in part, on a technique selected from the group consisting of;

a data-driven technique and a phonetic-based technique.

6. The method of claim 5 wherein the data-driven technique comprises:

deriving a phonemic confusion matrix, the deriving based, at least in part, on a phonemic recognition with an open phoneme grammar.

7. The method of claim 5 wherein the phonetic-based technique comprises:

representing each of a first and a second phoneme as a vector with each vector element corresponding to a distinctive phonetic feature of the respective phoneme;

weighting the vector elements, the weighting based, at least in part, on a relative frequency of each feature in a language, the language comprising the first and second phonemes; and

estimating the inter-phoneme distance between the first and second phonemes, the estimating based, at least in part, on the vectors of the first and second phonemes.

8. The method of claim 4 wherein calculating the inter-term pronunciation distance between a pair of translated terms comprises calculating an inter-term pronunciation confusability between the pair of translated terms.

9. The method of claim 8 wherein the inter-term pronunciation confusability is a modified Levenshtein distance between pronunciations of the pair of translated terms.

10. The method of claim 4 wherein calculating the search term pronunciation prominence comprises taking an average over a group of terms acoustically closest to the search term of an inter-term pronunciation distance between the search term and another term.

11. The method of claim 1 wherein the indexing weight assigned to the search term in the document is a multiplicative product of the calculated text-based indexing weight and the calculated pronunciation prominence.

12. A voice-to-text-search indexing server comprising:

a memory configured for storing an indexing weight assigned to a search term in a document, the document in a collection of documents; and

a processor operatively coupled to the memory and configured for calculating a text-based indexing weight for the search term in the document, for calculating a pronunciation prominence for the search term, and for assigning an indexing weight to the search term in the document, the indexing weight based, at least in part, on a mathematical combination of the calculated text-based indexing weight and the calculated pronunciation prominence.

13. The voice-to-text-search indexing server of claim 12 wherein calculating a text-based indexing weight for the search term in the document comprises:

calculating a term frequency for the search term in the document;

calculating an inverse document frequency for the search term in the collection of documents; and

calculating the text-based indexing weight for the search term in the document by mathematically combining the calculated term frequency and the calculated inverse document frequency.

14. The voice-to-text-search indexing server of claim 12 wherein calculating a text-based indexing weight for the search term in the document comprises:

calculating a term frequency for the search term in the document;

calculating a discrimination value for the search term in the collection of documents; and

calculating the text-based indexing weight for the search term in the document by mathematically combining the calculated term frequency and the calculated discrimination value.

15. The voice-to-text-search indexing server of claim 12 wherein calculating a pronunciation prominence for the search term comprises:

translating terms in the documents in the collection of documents into phonetic pronunciations;

calculating inter-term pronunciation distances between pairs of the translated terms, the calculating based, at least in part, on inter-phoneme distances; and

calculating the search term pronunciation prominence, the calculating based, at least in part, on inter-term pronunciation distances.

16. The voice-to-text-search indexing server of claim 15 further comprising:

calculating an inter-phoneme distance, the calculating based, at least in part, on a technique selected from the group consisting of;

a data-driven technique and a phonetic-based technique.

17. The voice-to-text-search indexing server of claim 16 wherein the data-driven technique comprises:

deriving a phonemic confusion matrix, the deriving based, at least in part, on a phonemic recognition with an open phoneme grammar.

18. The voice-to-text-search indexing server of claim 16 wherein the phonetic-based technique comprises:

representing each of a first and a second phoneme as a vector with each vector element corresponding to a distinctive phonetic feature of the respective phoneme;

weighting the vector elements, the weighting based, at least in part, on a relative frequency of each feature in a language, the language comprising the first and second phonemes; and

estimating the inter-phoneme distance between the first and second phonemes, the estimating based, at least in part, on the vectors of the first and second phonemes.

19. The voice-to-text-search indexing server of claim 15 wherein calculating the inter-term pronunciation distance between a pair of translated terms comprises calculating an inter-term pronunciation confusability between the pair of translated terms.

20. The voice-to-text-search indexing server of claim 19 wherein the inter-term pronunciation confusability is a modified Levenshtein distance between pronunciations of the pair of translated terms.

21. The voice-to-text-search indexing server of claim 15 wherein calculating the search term pronunciation prominence comprises taking an average over a group of terms acoustically closest to the search term of an inter-term pronunciation distance between the search term and another term.

22. The voice-to-text-search indexing server of claim 12 wherein the indexing weight assigned to the search term in the document is a multiplicative product of the calculated text-based indexing weight and the calculated pronunciation prominence.